Abstract
BackgroundCo-expression has been widely used to identify novel regulatory relationships using high throughput measurements, such as microarray and RNA-seq data. Evaluation studies on co-expression network analysis methods mostly focus on networks of small or medium size of up to a few hundred nodes. For large networks, simulated expression data usually consist of hundreds or thousands of profiles with different perturbations or knock-outs, which is uncommon in real experiments due to their cost and the amount of work required. Thus, the performances of co-expression network analysis methods on large co-expression networks consisting of a few thousand nodes, with only a small number of profiles with a single perturbation, which more accurately reflect normal experimental conditions, are generally uncharacterized and unknown.MethodsWe proposed a novel network inference methods based on Relevance Low order Partial Correlation (RLowPC). RLowPC method uses a two-step approach to select on the high-confidence edges first by reducing the search space by only picking the top ranked genes from an intial partial correlation analysis and, then computes the partial correlations in the confined search space by only removing the linear dependencies from the shared neighbours, largely ignoring the genes showing lower association.ResultsWe selected six co-expression-based methods with good performance in evaluation studies from the literature: Partial correlation, PCIT, ARACNE, MRNET, MRNETB and CLR. The evaluation of these methods was carried out on simulated time-series data with various network sizes ranging from 100 to 3000 nodes. Simulation results show low precision and recall for all of the above methods for large networks with a small number of expression profiles. We improved the inference significantly by refinement of the top weighted edges in the pre-inferred partial correlation networks using RLowPC. We found improved performance by partitioning large networks into smaller co-expressed modules when assessing the method performance within these modules.ConclusionsThe evaluation results show that current methods suffer from low precision and recall for large co-expression networks where only a small number of profiles are available. The proposed RLowPC method effectively reduces the indirect edges predicted as regulatory relationships and increases the precision of top ranked predictions. Partitioning large networks into smaller highly co-expressed modules also helps to improve the performance of network inference methods.The RLowPC R package for network construction, refinement and evaluation is available at GitHub: https://github.com/wyguo/RLowPC.
Highlights
Co-expression has been widely used to identify novel regulatory relationships using high throughput measurements, such as microarray and RNA-seq data
The inference methods: Scan Bayesian Model Averaging (ScanBMA), Gene Network Inference with Ensemble of trees (GENIE3) and Minimum Redundancy NETworks using Backward elimination (MRNETB) were the top performers in three different studies using the DREAM4 challenge time-series data, which is composed of five perturbation experiments for size 10 networks and ten perturbation experiments for size 100 networks, each with 21 time points [24,25,26,27] (Additional file 1: Table S1)
Relevance Low order Partial Correlation (RLowPC) significantly improves the precision and recall in top predictions Figure 2 illustrates the average Partial area under the precision-recall curve (pAUPR) values, which are the partial Area Under Precision against Recall of the top 1000 predictions, for the different methods for different network sizes
Summary
Co-expression has been widely used to identify novel regulatory relationships using high throughput measurements, such as microarray and RNA-seq data. Due to its low computational complexity as well as lower requirements for the number of samples, co-expression network analysis has been widely used to infer gene regulatory networks from high throughput expression data, such as microarray or RNA-seq data [10, 16,17,18,19]. The inference methods: Scan Bayesian Model Averaging (ScanBMA), Gene Network Inference with Ensemble of trees (GENIE3) and Minimum Redundancy NETworks using Backward elimination (MRNETB) were the top performers in three different studies using the DREAM4 challenge time-series data, which is composed of five perturbation experiments for size 10 networks and ten perturbation experiments for size 100 networks, each with 21 time points [24,25,26,27] (Additional file 1: Table S1). The Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE) method had a much better performance than BN and RN on expression data with 1000 samples simulated from size 100 networks [28] while MRNET was the top ranked method when compared to the RN, ARACNE and Context likelihood or relatedness (CLR) methods on 30 datasets with different network sizes (from 100 to 1000) and sample sizes (from 100 to 1000) [29] (Additional file 1: Table S1)
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